
where \(\mathbf {M}\)  is a random binary mask with the percentage of zeros determined by the pixel dropout ratio. This is analogous to the case of training deep neural networks, where Stochastic Gradient Descent (SGD) helps in escaping from sharp local minima [1]}. Here, the washed out reconstructions correspond to sharp local minima owing to the strong correlation between pixels. We demonstrate the effect of the amount of pixel dropout on the reconstructions in Section REF .
